Research map / Bandits and online learning / Multi-armed bandits
Crawling the Community Structure of Multiplex Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use MCS instead of best single baseline, RQR at 20% budget | MCS · TwitterOW/TwitterTR/TwitterSC/DBLP | community similarity: 9% to 25% | Helped |
| use MCS instead of best single baseline, UQR at 20% budget | MCS · TwitterOW/TwitterTR/TwitterSC/DBLP | community similarity: 8% to 49% | Helped |
| MCS vs ARW on TwitterOW (RQR) | MCS · TwitterOW | community similarity: 0.592 -> 0.685 | Helped |
| MCS vs ARW on TwitterTR (RQR) | MCS · TwitterTR | community similarity: 0.592 -> 0.721 | Helped |
| MCS vs SMD on DBLP (RQR) | MCS · DBLP | community similarity: 0.554 -> 0.679 | Helped |
| MCS vs MMD on DBLP (UQR) | MCS · DBLP | community similarity: 0.517 -> 0.628 | Helped |
| remove CBandit and RBandit, keep only two bandits (ablation) | MCS · community similarity vs budget | community similarity | Helped |
| replace epsilon-greedy with epsilon-decreasing, VDBE or UCB1 (ablation) | MCS · community similarity | community similarity | No clear effect |
| increase number of synthetic cheap layers beyond four | MCS · DBLP | community similarity | Mixed |
| MCS vs random walk on real Twitter API over 168 hours | MCS · Twitter API | number of edges found | Helped |
Closest papers by meaning
- Harnessing Multiple Correlated Networks for Exact Community Recovery (NeurIPS 2024 · similarity 0.87)
- Community Focusing: Yet Another Query-Dependent Community Detection (AAAI 2019 · similarity 0.87)
- Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning (ICML 2021 · similarity 0.86)
- Query-Limited Community Recovery in Stochastic Block Models (NeurIPS 2026 · similarity 0.85)
- An Efficient Local Search Approach for Polarized Community Discovery in Signed Networks (NeurIPS 2025 · similarity 0.85)
- Learning Persistent Community Structures in Dynamic Networks via Topological Data Analysis (AAAI 2024 · similarity 0.84)
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